•Forecasted weekly sales of Walmart during holiday and non-holiday weeks across various regions to discover which factors impact sales and derive actionable insights to enhance the sales
•Analyzed predictors using Mosaic plots, scatterplots, incorporated data cleansing through outlier rectification and feature selection in SAS JMP. Generated ensemble models such as Partition Tree, Random forest, KNN Cluster to compare accuracy scores and error metrics against linear models in SAS for accurate predictions in sales. Random Forest was identified as best model with 89% accuracy
2. Problem Statement
The objective is to predict the weekly sales of a Retail
Store looking at previous years performance per Store
on a weekly basis.
To analyze how internal and external factors can affect
the Weekly Sales in the future.
To Provide recommended actions based on the
insights drawn, with prioritization placed on largest
business impact
14. • Identified missing data and outliers
• Handled missing items using Normal
Imputation
• Reduced outliers using Normal 3
Transformation
• Target variable scaled down by a factor of
1000
DATA PRE-PROCESSING
27. What Next…
Impact of fuel price on sales on a weekly basis with help of
indicators such as distance of consumer from the stores
Geographical co-ordinates of the store which can help analyze the
impact of temperature on consumer purchase patterns
Extend the exploration to market basket analysis using additional
indicators such as products/consumer goods, departments and
previous and current orders of consumers